Energy efficiency optimization analysis of a ground source heat pump system based on neural networks and genetic algorithms

被引：0
作者：
Wei, Shanming [1 ]
Wang, Haibo [2 ]
Tian, Yanfa [3 ]
Man, Xubo [1 ]
Wang, Yanshi [1 ]
Zhou, Shiyu [2 ]
机构：
[1] Shandong Prov Geomineral Engn Explorat Inst, Shandong Prov Bur Geol & Mineral Resources, Inst Hydrogeol & Engn Geol 801, Jinan 250013, Peoples R China
[2] Shandong Jianzhu Univ, Sch Thermal Engn, Jinan 250101, Peoples R China
[3] Shandong Huake Planning Architectural Design Co L, Liaocheng 252000, Shandong, Andorra
来源：
GEOTHERMAL ENERGY | 2024年 / 12卷 / 01期
关键词：
Ground source heat pump; Back propagation neural network; Genetic algorithm; Energy consumption; Optimization;
D O I：
10.1186/s40517-024-00325-2
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
This paper reports on the performance of a ground source heat pump (GSHP) system located in Shandong Province, China. The system operation data were monitored and collected by a data collection system. According to the analysis of the accumulated operational data, it was found that the GSHP system showed a relative higher COP in cooling season of 2023 than that of 2022 due to the change of supplying water temperature at ground-source side. Based on the analyzed data, a BP neural network model for energy consumption prediction was established. Furthermore, genetic algorithm (GA) was used to optimize the control strategy on the basis of the energy consumption prediction model. Comparison between the artificial experience control strategy and the one optimized by the genetic algorithm was conducted. The results show that the optimization strategy of the genetic algorithm is superior in terms of energy saving, particularly in the load rate higher than 50%, in which, the average energy-saving rate reaches 39.66%. Within the load rate range of 30-50%, the energy-saving rate could also reach 7.84%.
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页数：15
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  • [1] Afroz Z., Shafiullah G.M., Urmee T., Shoeb M.A., Higgins G., Predictive modelling and optimization of HVAC systems using neural network and particle swarm optimization algorithm, Build Environ, 209, (2022)
  • [2] Barthwal M., Dhar A., Powar S., The techno-economic and environmental analysis of genetic algorithm (GA) optimized cold thermal energy storage (CTES) for air-conditioning applications, Appl Energy, 283, (2021)
  • [3] Chaturvedi S., Bhatt N., Gujar R., Patel D., Application of PSO and GA stochastic algorithms to select optimum building envelope and air conditioner size—a case of a residential building prototype, Mater Today Proc, 57, pp. 49-56, (2022)
  • [4] Garcia S., Ramirez-Gallego S., Luengo J., Benitez J.M., Herrera F., Big data preprocessing: methods and prospects, Big Data Anal, 1, pp. 1-22, (2016)
  • [5] Ground Thermal Response Test Report of Liaocheng Vocational School, 4, (2014)
  • [6] Han W., Lu Y., Comparison design method of ε value for fan coil units combined with fresh air conditioning system, Heating Ventilating & Air Conditioning, 5, pp. 80-83, (2002)
  • [7] Hu M., Xiao F., Price-responsive model-based optimal demand response control of inverter air conditioners using genetic algorithm, Appl Energy, 219, pp. 151-164, (2018)
  • [8] Li N., Comparison of the characteristics of the control strategies based on artificial neural network and genetic algorithm for air conditioning systems, J Build Eng, 66, (2023)
  • [9] Liu X., Lu S., Hughes P., Cai Z., A comparative study of the status of GSHP applications in the United States and China, Renewable Sustain Energy Rev, 48, pp. 558-570, (2015)
  • [10] Lund J.W., Boyd T.L., Direct utilization of geothermal energy 2015 worldwide review, Geothermics, 60, pp. 66-93, (2016)
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